One Memristor-One Electrolyte-Gated Transistor based High Energy-Efficient Dropout Neuronal Units

Abstract Artificial neural networks (ANN) have been extensively researched due to their significant energy-saving benefits. Hardware implementations of ANN with dropout function would be able to avoid the overfitting problem. This letter reports a dropout neuronal unit (1R1T-DNU) based on one memristor-one electrolyte-gated transistor with an ultralow energy consumption of 25 pJ/spike. A dropout neural network is constructed based on such device and has been verified by MNIST dataset, demonstrating high recognition accuracies (>90%) within a large range of dropout probabilities up to 40%. The running time can be reduced by increasing dropout probability without a significant loss in accuracy. Our results indicate the great potential of introducing such 1R1T-DNUs in full-hardware neural networks to enhance energy efficiency and to solve the overfitting problem.